Orthogonal locally discriminant projection algorithm for incremental data
Yibo Jiang · Journal of Optoelectronics·laser · 2013
The standard implementation of traditional projection algorithms takes all the training samples as the input data,which scales badly with the dataset size and makes computations for large samples application infeasible.We introduce a block optimization and batch alignment strategy to propose a novel locally discriminant projection(LDP) algorithm for solving this problem.The advantages of the proposed algorithm are:Firstly,it preserves the intra class structure of the manifold and maximizes margins between the data of different classes;Secondly,the final projection matrix of the proposed algorithm has the orthogonality property;Thirdly,there is no small sample size problem in this algorithm;Finally,LDP can be easily extended to the incremental LDP(ILDP) for learning the locally discriminant subspace with the newly inserted data by employing the singular value decomposition updating algorithm.The experimental data by employing the singular value decomposition updating algoirthm.The experimental results on COIL image database,USPS hand written digit database on ExYaleB face database demonstrate that ILDP has higher recognition rate compared with the classical ILDA,LSDA and MMP algorithms.Especially in the USPS database,ILDP reaches recognition rate of 90% while the others are all below 85%.Meanwhile,ILDP bears less computational cost,which needs only less than 0.5 s for training USPS database.